{"abstract":"Tiny pre-period samples exaggerate imbalance.","category":"Experiment statistics","checks":8,"contract":"Standardised mean difference = (mean_t - mean_c) / sqrt((var_c + var_t) / 2) with sample (n - 1) variances; imbalance iff |SMD| > threshold. Zero pooled variance -> [0.0, False] when means are equal else [None, True]. Fewer than two values in an arm -> None. Return [round(smd, 6), imbalance].","evaluation_group":"w2-experiment-statistics-pre-period-balance","failed_approach":"Dividing by n + 1 shrinks the variance further.","family":"w2-experiment-statistics-pre-period-balance-variance-divisor","id":"FA-74751","implementations":{"attempt":{"sha256":"c5e9fc3fe5edd0f25dc2ce3f4f3dd6b22011c8ec8b99d356d47993e448f6d0e2","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(pre_c, pre_t, threshold):\n    def moments(xs):\n        n = len(xs)\n        m = sum(xs) / n\n        return m, sum((x - m) ** 2 for x in xs) / (n + 1)\n    if len(pre_c) < 2 or len(pre_t) < 2:\n        return None\n    mc, vc = moments(pre_c)\n    mt, vt = moments(pre_t)\n    pooled = math.sqrt((vc + vt) / 2)\n    if pooled == 0:\n        return [0.0, False] if mt == mc else [None, True]\n    smd = (mt - mc) / pooled\n    return [round(smd, 6), abs(smd) > threshold]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('treatment arm more variable', [[1, 2, 3], [0, 4, 8], 0.25], [0.685994, True]),\n  ('SMD exactly at the tolerance passes', [[0, 2, 4], [1, 3, 5], 0.5], [0.5, False]),\n  ('negative imbalance is flagged', [[5, 6, 7], [1, 2, 3], 0.25], [-4.0, True]),\n  ('small arms', [[1, 3], [2, 6], 0.1], [0.894427, True]),\n  ('constant equal arms', [[4, 4], [4, 4], 0.1], [0.0, False]),\n  ('pre-period sample 1', [[1, 3, 6], [10, 3], 0.5], [0.806505, True]),\n  ('pre-period sample 2', [[5, 9, 3, 4], [3, 7], 0.1], [-0.091542, False]),\n  ('pre-period sample 3', [[6, 8, 3, 8, 9], [6, 5, 4, 10], 0.25], [-0.21898, False])],\n [('SMD exactly at the tolerance passes', [[0, 2, 4], [1, 3, 5], 0.5], [0.5, False]),\n  ('negative imbalance is flagged', [[5, 6, 7], [1, 2, 3], 0.25], [-4.0, True]),\n  ('small arms', [[1, 3], [2, 6], 0.1], [0.894427, True]),\n  ('constant equal arms', [[4, 4], [4, 4], 0.1], [0.0, False]),\n  ('constant different arms', [[4, 4], [5, 5], 0.1], [None, True]),\n  ('pre-period sample 4', [[5, 7], [2, 10, 7, 9, 9], 0.5], [0.564534, True]),\n  ('pre-period sample 6', [[8, 0], [8, 5, 6, 2], 0.1], [0.285831, True]),\n  ('pre-period sample 7', [[7, 0, 8, 6, 7], [9, 4, 5, 2, 2], 0.5], [-0.393496, False])],\n [('negative imbalance is flagged', [[5, 6, 7], [1, 2, 3], 0.25], [-4.0, True]),\n  ('small arms', [[1, 3], [2, 6], 0.1], [0.894427, True]),\n  ('constant equal arms', [[4, 4], [4, 4], 0.1], [0.0, False]),\n  ('constant different arms', [[4, 4], [5, 5], 0.1], [None, True]),\n  ('single observation arm', [[1], [1, 2], 0.1], None),\n  ('pre-period sample 11', [[8, 3, 4], [7, 3, 4], 0.1], [-0.140028, True]),\n  ('pre-period sample 12', [[4, 3, 3], [4, 0], 0.1], [-0.653197, True]),\n  ('pre-period sample 13', [[3, 5], [5, 7, 10, 7, 2], 0.1], [0.951143, True])],\n [('treatment arm more variable', [[1, 2, 3], [0, 4, 8], 0.25], [0.685994, True]),\n  ('small arms', [[1, 3], [2, 6], 0.1], [0.894427, True]),\n  ('constant equal arms', [[4, 4], [4, 4], 0.1], [0.0, False]),\n  ('constant different arms', [[4, 4], [5, 5], 0.1], [None, True]),\n  ('single observation arm', [[1], [1, 2], 0.1], None),\n  ('pre-period sample 16', [[3, 5, 5, 2], [4, 11], 0.1], [1.025379, True]),\n  ('pre-period sample 17', [[6, 1, 1, 4, 1], [7, 10, 8, 4], 0.25], [1.934982, True]),\n  ('pre-period sample 19', [[3, 1, 3, 7], [5, 4, 9], 0.1], [0.968246, True])],\n [('treatment arm more variable', [[1, 2, 3], [0, 4, 8], 0.25], [0.685994, True]),\n  ('SMD exactly at the tolerance passes', [[0, 2, 4], [1, 3, 5], 0.5], [0.5, False]),\n  ('constant equal arms', [[4, 4], [4, 4], 0.1], [0.0, False]),\n  ('constant different arms', [[4, 4], [5, 5], 0.1], [None, True]),\n  ('single observation arm', [[1], [1, 2], 0.1], None),\n  ('pre-period sample 21', [[3, 2, 5, 0], [0, 6], 0.5], [0.149626, False]),\n  ('pre-period sample 22', [[5, 8, 9, 5], [11, 8, 5, 6], 0.5], [0.316228, False]),\n  ('pre-period sample 26', [[6, 7, 1, 4, 9], [3, 4, 11, 6, 10], 0.25], [0.422116, True])]]\nfor label, args, expected in fixtures[N - 1]:\n    check(label, solve(*args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"broken":{"sha256":"8ca58197bb5d016cbee843672d8628828bcac9b48a742ccbf5f89b5f759bc628","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(pre_c, pre_t, threshold):\n    def moments(xs):\n        n = len(xs)\n        m = sum(xs) / n\n        return m, sum((x - m) ** 2 for x in xs) / n\n    if len(pre_c) < 2 or len(pre_t) < 2:\n        return None\n    mc, vc = moments(pre_c)\n    mt, vt = moments(pre_t)\n    pooled = math.sqrt((vc + vt) / 2)\n    if pooled == 0:\n        return [0.0, False] if mt == mc else [None, True]\n    smd = (mt - mc) / pooled\n    return [round(smd, 6), abs(smd) > threshold]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('treatment arm more variable', [[1, 2, 3], [0, 4, 8], 0.25], [0.685994, True]),\n  ('SMD exactly at the tolerance passes', [[0, 2, 4], [1, 3, 5], 0.5], [0.5, False]),\n  ('negative imbalance is flagged', [[5, 6, 7], [1, 2, 3], 0.25], [-4.0, True]),\n  ('small arms', [[1, 3], [2, 6], 0.1], [0.894427, True]),\n  ('constant equal arms', [[4, 4], [4, 4], 0.1], [0.0, False]),\n  ('pre-period sample 1', [[1, 3, 6], [10, 3], 0.5], [0.806505, True]),\n  ('pre-period sample 2', [[5, 9, 3, 4], [3, 7], 0.1], [-0.091542, False]),\n  ('pre-period sample 3', [[6, 8, 3, 8, 9], [6, 5, 4, 10], 0.25], [-0.21898, False])],\n [('SMD exactly at the tolerance passes', [[0, 2, 4], [1, 3, 5], 0.5], [0.5, False]),\n  ('negative imbalance is flagged', [[5, 6, 7], [1, 2, 3], 0.25], [-4.0, True]),\n  ('small arms', [[1, 3], [2, 6], 0.1], [0.894427, True]),\n  ('constant equal arms', [[4, 4], [4, 4], 0.1], [0.0, False]),\n  ('constant different arms', [[4, 4], [5, 5], 0.1], [None, True]),\n  ('pre-period sample 4', [[5, 7], [2, 10, 7, 9, 9], 0.5], [0.564534, True]),\n  ('pre-period sample 6', [[8, 0], [8, 5, 6, 2], 0.1], [0.285831, True]),\n  ('pre-period sample 7', [[7, 0, 8, 6, 7], [9, 4, 5, 2, 2], 0.5], [-0.393496, False])],\n [('negative imbalance is flagged', [[5, 6, 7], [1, 2, 3], 0.25], [-4.0, True]),\n  ('small arms', [[1, 3], [2, 6], 0.1], [0.894427, True]),\n  ('constant equal arms', [[4, 4], [4, 4], 0.1], [0.0, False]),\n  ('constant different arms', [[4, 4], [5, 5], 0.1], [None, True]),\n  ('single observation arm', [[1], [1, 2], 0.1], None),\n  ('pre-period sample 11', [[8, 3, 4], [7, 3, 4], 0.1], [-0.140028, True]),\n  ('pre-period sample 12', [[4, 3, 3], [4, 0], 0.1], [-0.653197, True]),\n  ('pre-period sample 13', [[3, 5], [5, 7, 10, 7, 2], 0.1], [0.951143, True])],\n [('treatment arm more variable', [[1, 2, 3], [0, 4, 8], 0.25], [0.685994, True]),\n  ('small arms', [[1, 3], [2, 6], 0.1], [0.894427, True]),\n  ('constant equal arms', [[4, 4], [4, 4], 0.1], [0.0, False]),\n  ('constant different arms', [[4, 4], [5, 5], 0.1], [None, True]),\n  ('single observation arm', [[1], [1, 2], 0.1], None),\n  ('pre-period sample 16', [[3, 5, 5, 2], [4, 11], 0.1], [1.025379, True]),\n  ('pre-period sample 17', [[6, 1, 1, 4, 1], [7, 10, 8, 4], 0.25], [1.934982, True]),\n  ('pre-period sample 19', [[3, 1, 3, 7], [5, 4, 9], 0.1], [0.968246, True])],\n [('treatment arm more variable', [[1, 2, 3], [0, 4, 8], 0.25], [0.685994, True]),\n  ('SMD exactly at the tolerance passes', [[0, 2, 4], [1, 3, 5], 0.5], [0.5, False]),\n  ('constant equal arms', [[4, 4], [4, 4], 0.1], [0.0, False]),\n  ('constant different arms', [[4, 4], [5, 5], 0.1], [None, True]),\n  ('single observation arm', [[1], [1, 2], 0.1], None),\n  ('pre-period sample 21', [[3, 2, 5, 0], [0, 6], 0.5], [0.149626, False]),\n  ('pre-period sample 22', [[5, 8, 9, 5], [11, 8, 5, 6], 0.5], [0.316228, False]),\n  ('pre-period sample 26', [[6, 7, 1, 4, 9], [3, 4, 11, 6, 10], 0.25], [0.422116, True])]]\nfor label, args, expected in fixtures[N - 1]:\n    check(label, solve(*args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"fixed":{"sha256":"9a029c9e6b460d1e3eaae0d68fdde861da68bd3cca336e29b35cd5c871121d92","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(pre_c, pre_t, threshold):\n    def moments(xs):\n        n = len(xs)\n        m = sum(xs) / n\n        return m, sum((x - m) ** 2 for x in xs) / (n - 1)\n    if len(pre_c) < 2 or len(pre_t) < 2:\n        return None\n    mc, vc = moments(pre_c)\n    mt, vt = moments(pre_t)\n    pooled = math.sqrt((vc + vt) / 2)\n    if pooled == 0:\n        return [0.0, False] if mt == mc else [None, True]\n    smd = (mt - mc) / pooled\n    return [round(smd, 6), abs(smd) > threshold]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('treatment arm more variable', [[1, 2, 3], [0, 4, 8], 0.25], [0.685994, True]),\n  ('SMD exactly at the tolerance passes', [[0, 2, 4], [1, 3, 5], 0.5], [0.5, False]),\n  ('negative imbalance is flagged', [[5, 6, 7], [1, 2, 3], 0.25], [-4.0, True]),\n  ('small arms', [[1, 3], [2, 6], 0.1], [0.894427, True]),\n  ('constant equal arms', [[4, 4], [4, 4], 0.1], [0.0, False]),\n  ('pre-period sample 1', [[1, 3, 6], [10, 3], 0.5], [0.806505, True]),\n  ('pre-period sample 2', [[5, 9, 3, 4], [3, 7], 0.1], [-0.091542, False]),\n  ('pre-period sample 3', [[6, 8, 3, 8, 9], [6, 5, 4, 10], 0.25], [-0.21898, False])],\n [('SMD exactly at the tolerance passes', [[0, 2, 4], [1, 3, 5], 0.5], [0.5, False]),\n  ('negative imbalance is flagged', [[5, 6, 7], [1, 2, 3], 0.25], [-4.0, True]),\n  ('small arms', [[1, 3], [2, 6], 0.1], [0.894427, True]),\n  ('constant equal arms', [[4, 4], [4, 4], 0.1], [0.0, False]),\n  ('constant different arms', [[4, 4], [5, 5], 0.1], [None, True]),\n  ('pre-period sample 4', [[5, 7], [2, 10, 7, 9, 9], 0.5], [0.564534, True]),\n  ('pre-period sample 6', [[8, 0], [8, 5, 6, 2], 0.1], [0.285831, True]),\n  ('pre-period sample 7', [[7, 0, 8, 6, 7], [9, 4, 5, 2, 2], 0.5], [-0.393496, False])],\n [('negative imbalance is flagged', [[5, 6, 7], [1, 2, 3], 0.25], [-4.0, True]),\n  ('small arms', [[1, 3], [2, 6], 0.1], [0.894427, True]),\n  ('constant equal arms', [[4, 4], [4, 4], 0.1], [0.0, False]),\n  ('constant different arms', [[4, 4], [5, 5], 0.1], [None, True]),\n  ('single observation arm', [[1], [1, 2], 0.1], None),\n  ('pre-period sample 11', [[8, 3, 4], [7, 3, 4], 0.1], [-0.140028, True]),\n  ('pre-period sample 12', [[4, 3, 3], [4, 0], 0.1], [-0.653197, True]),\n  ('pre-period sample 13', [[3, 5], [5, 7, 10, 7, 2], 0.1], [0.951143, True])],\n [('treatment arm more variable', [[1, 2, 3], [0, 4, 8], 0.25], [0.685994, True]),\n  ('small arms', [[1, 3], [2, 6], 0.1], [0.894427, True]),\n  ('constant equal arms', [[4, 4], [4, 4], 0.1], [0.0, False]),\n  ('constant different arms', [[4, 4], [5, 5], 0.1], [None, True]),\n  ('single observation arm', [[1], [1, 2], 0.1], None),\n  ('pre-period sample 16', [[3, 5, 5, 2], [4, 11], 0.1], [1.025379, True]),\n  ('pre-period sample 17', [[6, 1, 1, 4, 1], [7, 10, 8, 4], 0.25], [1.934982, True]),\n  ('pre-period sample 19', [[3, 1, 3, 7], [5, 4, 9], 0.1], [0.968246, True])],\n [('treatment arm more variable', [[1, 2, 3], [0, 4, 8], 0.25], [0.685994, True]),\n  ('SMD exactly at the tolerance passes', [[0, 2, 4], [1, 3, 5], 0.5], [0.5, False]),\n  ('constant equal arms', [[4, 4], [4, 4], 0.1], [0.0, False]),\n  ('constant different arms', [[4, 4], [5, 5], 0.1], [None, True]),\n  ('single observation arm', [[1], [1, 2], 0.1], None),\n  ('pre-period sample 21', [[3, 2, 5, 0], [0, 6], 0.5], [0.149626, False]),\n  ('pre-period sample 22', [[5, 8, 9, 5], [11, 8, 5, 6], 0.5], [0.316228, False]),\n  ('pre-period sample 26', [[6, 7, 1, 4, 9], [3, 4, 11, 6, 10], 0.25], [0.422116, True])]]\nfor label, args, expected in fixtures[N - 1]:\n    check(label, solve(*args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"}},"limitations":"A deterministic toy experiment-analysis model with a stipulated contract; results are rounded and are not a substitute for a validated statistics package. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.","method":"Deterministic executable model with adversarial boundary fixtures.","provenance":{"created_by":"Failure Map","dependencies":"Python standard library","family":"w2-experiment-statistics-pre-period-balance-variance-divisor","generated_at":"2026-09-29T14:48:59.671798+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Pre-period imbalance warns that randomisation or logging went wrong before any effect is read.","repair":"Use the n - 1 divisor.","root_cause":"Variances divide by n instead of n - 1.","sha256":"f78c4edafff634452e6936fa16933bff39ba8832006ce2038d4e6a9e533ebac8","title":"Pre-period balance check: Balance uses population variances · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":38.617,"exit_code":1,"observations":[{"actual":[0.970143,true],"check":"treatment arm more variable","expected":[0.685994,true],"passed":false},{"actual":[0.707107,true],"check":"SMD exactly at the tolerance passes","expected":[0.5,false],"passed":false},{"actual":[-5.656854,true],"check":"negative imbalance is flagged","expected":[-4.0,true],"passed":false},{"actual":[1.549193,true],"check":"small arms","expected":[0.894427,true],"passed":false},{"actual":[0.0,false],"check":"constant equal arms","expected":[0.0,false],"passed":true},{"actual":[1.330266,true],"check":"pre-period sample 1","expected":[0.806505,true],"passed":false},{"actual":[-0.135416,true],"check":"pre-period sample 2","expected":[-0.091542,false],"passed":false},{"actual":[-0.275863,true],"check":"pre-period sample 3","expected":[-0.21898,false],"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"treatment arm more variable\", \"actual\": [0.970143, true], \"expected\": [0.685994, true], \"passed\": false}, {\"check\": \"SMD exactly at the tolerance passes\", \"actual\": [0.707107, true], \"expected\": [0.5, false], \"passed\": false}, {\"check\": \"negative imbalance is flagged\", \"actual\": [-5.656854, true], \"expected\": [-4.0, true], \"passed\": false}, {\"check\": \"small arms\", \"actual\": [1.549193, true], \"expected\": [0.894427, true], \"passed\": false}, {\"check\": \"constant equal arms\", \"actual\": [0.0, false], \"expected\": [0.0, false], \"passed\": true}, {\"check\": \"pre-period sample 1\", \"actual\": [1.330266, true], \"expected\": [0.806505, true], \"passed\": false}, {\"check\": \"pre-period sample 2\", \"actual\": [-0.135416, true], \"expected\": [-0.091542, false], \"passed\": false}, {\"check\": \"pre-period sample 3\", \"actual\": [-0.275863, true], \"expected\": [-0.21898, false], \"passed\": false}], \"passed\": false}\n"},"broken":{"elapsed_ms":38.112,"exit_code":1,"observations":[{"actual":[0.840168,true],"check":"treatment arm more variable","expected":[0.685994,true],"passed":false},{"actual":[0.612372,true],"check":"SMD exactly at the tolerance passes","expected":[0.5,false],"passed":false},{"actual":[-4.898979,true],"check":"negative imbalance is flagged","expected":[-4.0,true],"passed":false},{"actual":[1.264911,true],"check":"small arms","expected":[0.894427,true],"passed":false},{"actual":[0.0,false],"check":"constant equal arms","expected":[0.0,false],"passed":true},{"actual":[1.103421,true],"check":"pre-period sample 1","expected":[0.806505,true],"passed":false},{"actual":[-0.116642,true],"check":"pre-period sample 2","expected":[-0.091542,false],"passed":false},{"actual":[-0.249133,false],"check":"pre-period sample 3","expected":[-0.21898,false],"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"treatment arm more variable\", \"actual\": [0.840168, true], \"expected\": [0.685994, true], \"passed\": false}, {\"check\": \"SMD exactly at the tolerance passes\", \"actual\": [0.612372, true], \"expected\": [0.5, false], \"passed\": false}, {\"check\": \"negative imbalance is flagged\", \"actual\": [-4.898979, true], \"expected\": [-4.0, true], \"passed\": false}, {\"check\": \"small arms\", \"actual\": [1.264911, true], \"expected\": [0.894427, true], \"passed\": false}, {\"check\": \"constant equal arms\", \"actual\": [0.0, false], \"expected\": [0.0, false], \"passed\": true}, {\"check\": \"pre-period sample 1\", \"actual\": [1.103421, true], \"expected\": [0.806505, true], \"passed\": false}, {\"check\": \"pre-period sample 2\", \"actual\": [-0.116642, true], \"expected\": [-0.091542, false], \"passed\": false}, {\"check\": \"pre-period sample 3\", \"actual\": [-0.249133, false], \"expected\": [-0.21898, false], \"passed\": false}], \"passed\": false}\n"},"fixed":{"elapsed_ms":39.176,"exit_code":0,"observations":[{"actual":[0.685994,true],"check":"treatment arm more variable","expected":[0.685994,true],"passed":true},{"actual":[0.5,false],"check":"SMD exactly at the tolerance passes","expected":[0.5,false],"passed":true},{"actual":[-4.0,true],"check":"negative imbalance is flagged","expected":[-4.0,true],"passed":true},{"actual":[0.894427,true],"check":"small arms","expected":[0.894427,true],"passed":true},{"actual":[0.0,false],"check":"constant equal arms","expected":[0.0,false],"passed":true},{"actual":[0.806505,true],"check":"pre-period sample 1","expected":[0.806505,true],"passed":true},{"actual":[-0.091542,false],"check":"pre-period sample 2","expected":[-0.091542,false],"passed":true},{"actual":[-0.21898,false],"check":"pre-period sample 3","expected":[-0.21898,false],"passed":true}],"passed":true,"stderr":"","stdout":"{\"observations\": [{\"check\": \"treatment arm more variable\", \"actual\": [0.685994, true], \"expected\": [0.685994, true], \"passed\": true}, {\"check\": \"SMD exactly at the tolerance passes\", \"actual\": [0.5, false], \"expected\": [0.5, false], \"passed\": true}, {\"check\": \"negative imbalance is flagged\", \"actual\": [-4.0, true], \"expected\": [-4.0, true], \"passed\": true}, {\"check\": \"small arms\", \"actual\": [0.894427, true], \"expected\": [0.894427, true], \"passed\": true}, {\"check\": \"constant equal arms\", \"actual\": [0.0, false], \"expected\": [0.0, false], \"passed\": true}, {\"check\": \"pre-period sample 1\", \"actual\": [0.806505, true], \"expected\": [0.806505, true], \"passed\": true}, {\"check\": \"pre-period sample 2\", \"actual\": [-0.091542, false], \"expected\": [-0.091542, false], \"passed\": true}, {\"check\": \"pre-period sample 3\", \"actual\": [-0.21898, false], \"expected\": [-0.21898, false], \"passed\": true}], \"passed\": true}\n"}},"verified":true,"visibility":"public"}